我有以下代码来建模回归并将摘要打印到日志文件
#Finding the model fit using the multiple regression
fit = smf.ols(self.formula_string, data=df_train).fit()
fit_parameters = str(fit.params)
fit_summary = str(fit.summary())
logger.info('fit_summary' + fit_summary)
正如我们所知，摘要有一个表格，后面是一个网格。可以单独将摘要的网格部分（在下面的示例图像中为蓝色）转换为 HTML 文件吗？
OLS 由 3 个单独的summary表构建而成。每个表格都可以单独转换为字符串/文本、html 或 latex
res是下面fit方法返回的OLS结果实例
>>> summ = res.summary()
>>> dir(summ)
['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__',
'__format__', '__ge__', '__getattribute__', '__gt__', '__hash__', '__init__',
'__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__',
'__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__',
'__subclasshook__', '__weakref__', '_repr_html_', 'add_extra_txt',
'add_table_2cols', 'add_table_params', 'as_csv', 'as_html', 'as_latex',
'as_text', 'extra_txt', 'tables']
>>> len(summ.tables)
3
>>> summ.tables[1].as_html()
'<table class="simpletable">\n<tr>\n <td></td> <th>coef</th> <th>std err</th> <th>t</th> <th>P>|t|</th> <th>[0.025</th> <th>0.975]</th> \n</tr>\n<tr>\n <th>C(Region)[C]</th> <td> 38.6517</td> <td> 9.456</td> <td> 4.087</td> <td> 0.000</td> <td> 19.826</td> <td> 57.478</td>\n</tr>\n<tr>\n <th>C(Region)[E]</th> <td> 23.2239</td> <td> 14.931</td> <td> 1.555</td> <td> 0.124</td> <td> -6.501</td> <td> 52.949</td>\n</tr>\n<tr>\n <th>C(Region)[N]</th> <td> 28.6347</td> <td> 13.127</td> <td> 2.181</td> <td> 0.032</td> <td> 2.501</td> <td> 54.769</td>\n</tr>\n<tr>\n <th>C(Region)[S]</th> <td> 34.1034</td> <td> 10.370</td> <td> 3.289</td> <td> 0.002</td> <td> 13.459</td> <td> 54.748</td>\n</tr>\n<tr>\n <th>C(Region)[W]</th> <td> 28.5604</td> <td> 10.018</td> <td> 2.851</td> <td> 0.006</td> <td> 8.616</td> <td> 48.505</td>\n</tr>\n<tr>\n <th>Literacy</th> <td> -0.1858</td> <td> 0.210</td> <td> -0.886</td> <td> 0.378</td> <td> -0.603</td> <td> 0.232</td>\n</tr>\n<tr>\n <th>Wealth</th> <td> 0.4515</td> <td> 0.103</td> <td> 4.390</td> <td> 0.000</td> <td> 0.247</td> <td> 0.656</td>\n</tr>\n</table>'
>>> print(summ.tables[1])
================================================================================
coef std err t P>|t| [0.025 0.975]
--------------------------------------------------------------------------------
C(Region)[C] 38.6517 9.456 4.087 0.000 19.826 57.478
C(Region)[E] 23.2239 14.931 1.555 0.124 -6.501 52.949
C(Region)[N] 28.6347 13.127 2.181 0.032 2.501 54.769
C(Region)[S] 34.1034 10.370 3.289 0.002 13.459 54.748
C(Region)[W] 28.5604 10.018 2.851 0.006 8.616 48.505
Literacy -0.1858 0.210 -0.886 0.378 -0.603 0.232
Wealth 0.4515 0.103 4.390 0.000 0.247 0.656
================================================================================